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fritzprix/README.md

Hi there, I'm David (Doowoong) Lee | 이두웅 πŸ‘‹

Visits Gmail Website

πŸ’‘ About Me

I'm a passionate software engineer who thrives on diving deep into the fascinating world of low-level programming and cutting-edge AI. With an insatiable curiosity and a fast-learning mindset, I enjoy:

  • πŸš€ Exploring the intricate details of system architecture and low-level optimization
  • 🧠 Staying current with the latest AI research papers and implementing novel approaches
  • πŸ”¬ Bridging the gap between theoretical AI advances and practical implementations
  • πŸ”§ Tinkering with bare metal programming and system internals
  • πŸ’» Building everything from kernel modules to neural networks
  • πŸ“š Regular consumption and implementation of latest AI research papers

"The closer to the metal, the better the experience! And when combined with AI, the possibilities are endless!"

πŸ”¬ Core Interests

Low-Level Systems

  • System Programming & Kernel Development
  • Hardware-Software Interface
  • Performance Optimization
  • Memory Management Systems
  • Real-time Systems
  • Embedded Development

Artificial Intelligence

  • Large Language Models & Transformers
  • Neural Network Architecture Design
  • AI System Optimization
  • Machine Learning Infrastructure
  • AI Research Paper Implementation
  • Hardware Acceleration for AI

πŸ’» Tech Stack

Languages & AI Frameworks

Python C Rust CUDA Java JavaScript

AI & ML Tools

PyTorch Lightning Hugging Face TensorFlow scikit-learn Datasets

Low-Level & System Programming

Assembly Embedded Linux RTOS

Modern Development Stack

Docker Kubernetes MLflow Ray Weights & Biases

🎯 Projects & Research Interests

Low-Level Adventures

  • Building custom kernel modules
  • Developing real-time operating system components
  • Creating performance optimization tools
  • Implementing hardware interfaces

AI Explorations

  • Fine-tuning and optimizing transformer models
  • Implementing custom training pipelines with Lightning
  • Experimenting with efficient training techniques
  • Building scalable data processing pipelines with πŸ€— Datasets
  • Exploring model deployment optimization

πŸ“š Latest AI Paper Implementations

  • Transformer architecture optimizations
  • Efficient training techniques
  • Advanced attention mechanisms
  • Model scaling strategies

πŸ“Š GitHub Statistics

πŸ† GitHub Achievements

GitHub Streak

πŸ”­ Current Focus

  • Transformer model optimization and deployment
  • Neural network acceleration on custom hardware
  • Low-level system optimization
  • Operating system internals
  • Hardware abstraction layers
  • Exploring AI research papers

πŸ“« Connect With Me


def life_motto():
    return """
    Dive deep into the metal,
    Rise high with AI,
    Code for fun, learn forever!
    """

"The best way to predict the future is to implement it"

Pinned Loading

  1. TachyOS TachyOS Public

    Embedded OS for IoT

    C++ 17 4

  2. cdsl cdsl Public

    c data structure library

    C 5 3

  3. yarmi yarmi Public

    yet-another RMI Framework for Java

    Java 2 1

  4. robust-dns robust-dns Public

    little DNS tweak for ill configured network environment

    Java 1

  5. toyGPT toyGPT Public

    toyGPT - Hands-on project for learning Transformer and GPT model

    Jupyter Notebook